| --- |
| language: |
| - eu |
| pretty_name: ZelaiHandiClean 🤠 |
| task_categories: |
| - text-generation |
| size_categories: |
| - 100M<n<1B |
| --- |
| |
| ## Dataset Summary |
|
|
| ZelaiHandi-R (R = Refined) is the definitive and heavily denoised version of the original ZelaiHandi corpus, designed to maximize training efficiency and signal density under limited computational resources, it is also a version augmented with books from booktegui and Wikipedia articles to support high-efficiency language-modeling experiments. |
|
|
| This version prioritizes quality over raw size. A substantial amount of noise has been aggressively removed, including duplicated fragments, malformed extractions, structural scraping artifacts, low-signal text, formatting corruption, and non-linguistic content. |
|
|
| The objective of this release is not incremental cleaning, but training efficiency maximization. |
|
|
| By drastically reducing noise, this dataset: |
|
|
| - Increases information density per token |
|
|
| - Reduces wasted gradient updates |
|
|
| - Improves convergence speed |
|
|
| - Enhances training stability |
|
|
| Is especially effective for small and mid-sized models |
|
|
| Is explicitly optimized for compute-constrained environments |
|
|
| When computational resources are limited (low VRAM, small parameter counts, limited training budget), data quality becomes critical. |
| This definitive version is engineered to extract maximum performance per training step. |
|
|
| For example, this are the stats for Ekaia subset: |
|
|
| | Metric | Value | |
| |-----------------------------------------|------------:| |
| | Initial characters (Ekaia subset) | 14,480,942 | |
| | Final characters (after cleaning) | 12,746,071 | |
| | Overall cleaned | 11.98 % | |
| | Extra spaces removed | 0.03 % | |
| | Blank lines removed | 15.47 % | |
| | Non-linguistic characters removed | 11.96 % | |
|
|
| The cleaning process in this definitive release goes beyond surface normalization and focuses on improving the statistical integrity of the corpus. |
|
|
| ## Supported Tasks |
| - Causal language modeling |
| - Masked language modeling |
| - Next-sentence prediction |
| - Any downstream Basque NLP task (fine-tuning) |
|
|
| ## Languages |
| - Basque (`eu`) |
|
|
| ## Dataset Statistics |
|
|
| | Metric | Value | |
| |-----------------------------------------|------------:| |
| | Total words | 660 million | |
| | Disk size | 4.6 GB | |
| | Additional books scraped from Booktegui | 400 | |
| | Wikipedia articles added | +2,500 | |
|
|
| ## Dataset Structure |
| Each example in the JSONL files has the following schema: |
|
|
| { |
| "id": "unique for each document", |
| "periodico": "source", |
| "lugar": "geographic focus of the source", |
| "dominio": "type of content (articles, news, books…)", |
| "texto": "cleaned high-quality text used for training", |
| "licencia": "document license" |
| } |
|
|
| There is no specific train-val distinction in the dataset, but I would just take the dataset, divide it in 100 chunks of text and use 1 of them for val to make sure the model is generalising well. |
|
|
| ## Data Collection and Cleaning |
|
|
| 1. **Original Source** |
| - ZelaiHandi dataset (Basque news, books, articles) |
|
|
| 2. **Cleaning Steps** |
| - Removed extra whitespace and blank lines |
| - Normalized Unicode characters |
| - Stripped non-linguistic symbols (HTML tags, control characters) |
|
|
| 3. **Augmentation** |
| - +400 books (_liburuak_) scraped from Booktegui |
| - +2,500 articles from the Basque Wikipedia (Wikipediabi) |
| - Wikipedia Berria and Legebiltzarra datasets were ingested in **three parts** to avoid interface issues |
|
|
| --- |
|
|
| ## Considerations for Use |
|
|
| - All text is raw; you may wish to tokenize or further normalize per your model’s requirements. I have my own basque tokenizer that I provide you in my github. |
| - Maintain consistent train/validation splits for reproducible benchmarks. |
|
|
| --- |
|
|
| ## License |
|
|
| Various Creative Commons licenses (CC-BY, CC-BY-SA). |
| See each JSONL record’s `"licencia"` field for details. |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| > Orai NLP Teknologiak (2025). *ZelaiHandi + Booktegui + Wikipediabi Basque Corpus*. CC-BY-SA. |
|
|
| --- |
|
|
| ## Acknowledgements |
|
|
| Special thanks to: |
| - San Vicente, Iñaki & Urbizu |
| - Gorka & Corral |
| - Ander & Beloki |
| - Zuhaitz & Saralegi |
| - Xabier |
|
|
| …for creating the original ZelaiHandi dataset, which served as the foundation for this cleaned and slightly expanded corpus. |
|
|
| Tokenizer: https://github.com/Carlos141100/txamp-tokenizer-v12 |
|
|
